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Quantification of <i>Cylindrocarpon destructans</i> f. sp. <i>panacis</i> in soils by real‐time PCR

2006· article· en· W2115310056 on OpenAlexafffund
Gavin Kernaghan, R. D. Reeleder, S. M. T. Hoke

Bibliographic record

VenuePlant Pathology · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Resistance and Genetics
Canadian institutionsMount Saint Vincent UniversityAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsGinsengBiologyPathogenSoil waterCropHorticultureAgronomyRoot rotVeterinary medicineReal-time polymerase chain reactionBotanyMicrobiologyEcology

Abstract

fetched live from OpenAlex

Ginseng ( Panax quinquefolius ) is an important cash crop in various regions of North America, but yields are often reduced by various root pathogens. A quantitative real‐time PCR (qPCR) assay for Cylindrocarpon destructans f. sp. panacis (CDP), the cause of a root rot and replant disease which discourages successive cropping of ginseng on the same site, was developed to quantify the levels of this pathogen in soils previously cropped with ginseng. DNA was extracted from 5‐g samples of soil. In pasteurized soils which were re‐infested with varying levels of the pathogen, qPCR estimates of pathogen DNA were significantly correlated with disease severity ( r = 0·494) and with counts of colony‐forming units ( r = 0·620) obtained with an agar medium. In several naturally infested field soils, qPCR estimates of CDP‐DNA concentration were significantly correlated with disease severity ( r = 0·765) and these concentrations were estimated to range from 0 to 1·48 ng g −1 dried soil. A principal components analysis did not show any strong relationships between soil chemistry factors and the concentration of pathogen DNA. The approach outlined here allows the quantification of current populations of CDP in soil many years after ginseng cultivation and the prediction of disease severity in future crops. The method should be generally applicable to root diseases of many crops.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.192
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations41
Published2006
Admission routes2
Has abstractyes

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